Groundwater flow field model optimization method and system based on multi-parameter probability distribution

Abstract

The present invention relates to the field of groundwater flow field simulation technology, and in particular to a groundwater flow field model optimization method and system based on multi-parameter probability distribution, establishing a hydrogeological multi-parameter distribution model of soil samples in a target area to extract the spatial distribution characteristics of soil sample parameters in the target area; drawing a hydrogeological multi-parameter probability distribution curve, extracting and combining relevant parameter probability distribution functions, parameter median confidence interval value range and standard deviation to obtain multi-parameter combination characteristic values; using the multi-parameter combination characteristic values ​​as finite difference analysis software input, using the finite difference analysis software to calculate groundwater seepage to obtain a groundwater flow field model under multi-parameter probability distribution; using the parameter median as the model initial parameter and the confidence interval value range as the parameter inversion range to calibrate the model parameters to finally determine the groundwater flow field model parameters. The present invention can provide a reference basis for similar groundwater flow field numerical modeling, and optimize and improve the numerical simulation effect.

Description

Technical Field

[0001] The present invention relates to the technical field of groundwater flow field simulation, and in particular to a groundwater flow field model optimization method and system based on multi-parameter probability distribution. Background Art

[0002] Water resources are basic natural resources and strategic economic resources. They are one of the controlling factors of the ecological environment and an important part of the country's comprehensive national strength. In view of the environmental geological problems in the study area, in-depth hydrogeological investigation and research are carried out, scientific methods are used to study the temporal and spatial evolution of groundwater, and numerical simulation and scenario analysis and prediction of groundwater flow fields in the study area are carried out, which is of great significance to the sustainable development of groundwater resources. In recent years, with the rapid development of computer technology, finite difference numerical simulation technology has been widely used in groundwater flow field analysis. However, for the numerical simulation calculation results, multi-parameter analysis has not been carried out on areas with complex soil and lithology. Therefore, there are inaccurate and incomplete problems in the simulation calculation of groundwater flow fields, which in turn affects the expected effect of simulation. Summary of the invention

[0003] To this end, the present invention provides a groundwater flow field model optimization method and system based on multi-parameter probability distribution, which solves the problems of inaccurate and incomplete existing groundwater flow field simulation calculations and optimizes and improves the numerical simulation effect.

[0004] According to the design scheme provided by the present invention, a groundwater flow field model optimization method based on multi-parameter probability distribution is provided, comprising the following steps:

[0005] Establish a hydrogeological multi-parameter distribution model of soil samples in the target area to extract the spatial distribution characteristics of soil sample parameters in the target area, where the hydrogeological multi-parameters include permeability, water supply and water storage rate;

[0006] Draw the probability distribution curve of hydrogeological multi-parameters, extract and combine the probability distribution function of related parameters, the value range and standard deviation of the parameter median confidence interval, and obtain the multi-parameter combination characteristic value;

[0007] The multi-parameter combination eigenvalues ​​are used as inputs to the finite difference analysis software, and the groundwater seepage is calculated using the finite difference analysis software to obtain the groundwater flow field model under the multi-parameter probability distribution;

[0008] The model parameters were calibrated using the median of the parameters as the initial parameters of the model and the confidence interval value range as the parameter inversion range to ultimately determine the groundwater flow field model parameters.

[0009] As a groundwater flow field model optimization method based on multi-parameter probability distribution of the present invention, further, a hydrogeological multi-parameter distribution model is established, comprising:

[0010] First, conduct on-site exploration of the target area sections, drill each section and take layered samples to obtain soil samples;

[0011] Next, physical tests are carried out on the soil samples to obtain various parameter indicators, wherein the physical tests at least include particle grading analysis test, permeability coefficient test, water supply test and water storage rate test;

[0012] Then, the soil sample property classification is determined according to the particle grading, and the distribution model of soil permeability coefficient, water storage rate and water supply degree in the target area is established in combination with the soil permeability coefficient, water storage rate and water supply degree.

[0013] As a groundwater flow field model optimization method based on multi-parameter probability distribution of the present invention, further, in the physical test of the soil sample, a laser particle size analyzer is used and assisted by a rock and soil mineralization analyzer to carry out a particle grading analysis test to obtain the percentage content of clay, silt and sand particles in the soil sample and the mineral composition of the soil sample; and a constant head permeability test or a variable head permeability test is used to determine the permeability coefficient of the soil sample.

[0014] As a groundwater flow field model optimization method based on multi-parameter probability distribution of the present invention, further, the spatial distribution characteristics of soil sample parameters in the target area are extracted, including:

[0015] Firstly, according to the distribution model of three parameters, namely permeability coefficient, water storage rate and water supply degree, the partition of each parameter is determined;

[0016] Then, the three parameter distribution models are integrated and a probability distribution model is established, and the spatial distribution characteristics of soil samples in the target area are extracted based on the probability distribution model.

[0017] As a groundwater flow field model optimization method based on multi-parameter probability distribution of the present invention, further, drawing a hydrogeological multi-parameter probability distribution curve also includes: selecting a continuous distribution density function of each parameter according to the spatial heterogeneity characteristics of each parameter, wherein the continuous distribution density function includes: uniform distribution, exponential distribution, normal distribution or t distribution.

[0018] As a groundwater flow field model optimization method based on multi-parameter probability distribution of the present invention, further, finite difference analysis software is used to calculate groundwater seepage, including:

[0019] Firstly, the target area is meshed using finite difference analysis software, and the interval to be solved is divided into a finite number of grid nodes. The groundwater numerical model of the target area is constructed using the set of finite grid nodes, and the difference quotient of the node function value is used to establish the difference equation group.

[0020] Then, the initial and boundary conditions are used to solve the differential equations to obtain the groundwater flow field model in the target area.

[0021] As a groundwater flow field model optimization method based on multi-parameter probability distribution of the present invention, further, in solving the differential equations using initial conditions and boundary conditions, the differential equations are solved using initial conditions and boundary conditions based on parameter partitioning and value selection provided by the parameter space random distribution theoretical model, and combined with the simulated target area hydrogeological conditions and source and sink characteristics.

[0022] Furthermore, the present invention also provides a groundwater flow field model optimization system based on multi-parameter probability distribution, comprising: a feature extraction module, a feature combination module, a model construction module and a parameter determination module, wherein:

[0023] A feature extraction module is used to establish a hydrogeological multi-parameter distribution model of soil samples in the target area to extract the spatial distribution characteristics of soil sample parameters in the target area, wherein the hydrogeological multi-parameters include permeability, water supply and water storage rate;

[0024] The feature combination module is used to draw the probability distribution curve of hydrogeological multi-parameters, extract and combine the probability distribution function of related parameters, the value range and standard deviation of the parameter median confidence interval, and obtain the multi-parameter combination feature value;

[0025] A model building module is used to input the multi-parameter combination eigenvalues ​​into the finite difference analysis software, and calculate the groundwater seepage using the finite difference analysis software to obtain a groundwater flow field model under the multi-parameter probability distribution;

[0026] The parameter determination module is used to calibrate the model parameters by using the parameter median as the initial parameter of the model and the confidence interval value range as the parameter inversion range, so as to finally determine the groundwater flow field model parameters.

[0027] Beneficial effects of the present invention:

[0028] In view of the inaccuracy and incompleteness of the current finite difference numerical simulation technology in the numerical modeling of groundwater flow fields, the present invention can establish probability distribution functions of parameters such as permeability coefficient, water supply degree, and water storage rate based on the results of field and indoor hydrogeological parameter tests, analyze the random distribution characteristics of hydrogeological parameters of different aquifers, use the parameter median and confidence interval value range, and use finite difference analysis software to construct a groundwater numerical model, combine finite difference numerical simulation with probability statistics, effectively make up for the shortcomings of simply using the recommended values ​​of soil parameters, provide corresponding basis and reference for similar groundwater flow field numerical modeling, optimize and improve the numerical simulation effect, and have good application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1It is a schematic diagram of the optimization process of the groundwater flow field model based on multi-parameter probability distribution in the embodiment;

[0030] Figure 2 This is a schematic diagram of the overall numerical model of the preset target area in the embodiment;

[0031] Figure 3 The distribution law of permeability coefficient in different directions and function fitting in the embodiment are shown;

[0032] Figure 4 It is a schematic diagram of the water supply degree distribution law and function fitting in the embodiment;

[0033] Figure 5 It is a schematic diagram of the water storage rate distribution law and function fitting in the embodiment;

[0034] Figure 6 It is a schematic diagram of the numerical model of groundwater flow field in the embodiment;

[0035] Figure 7 Schematic diagram of the fitting between the measured water level at the observation point and the simulated water level in the embodiment. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solutions and advantages of the present invention clearer and more understandable, the present invention is further described in detail below in conjunction with the accompanying drawings and technical solutions.

[0037] In view of the problems existing in the existing numerical simulation described in the background technology, the present invention provides a groundwater flow field model optimization method based on multi-parameter probability distribution, see Figure 1 As shown, it includes the following steps:

[0038] S1, establish a hydrogeological multi-parameter distribution model of soil samples in the target area to extract the spatial distribution characteristics of soil sample parameters in the target area, wherein the hydrogeological multi-parameters include permeability, water supply and water storage rate.

[0039] Specifically, the hydrogeological multi-parameter distribution model can be designed to include the following contents:

[0040] First, conduct on-site exploration of the target area sections, drill each section and take layered samples to obtain soil samples;

[0041] Next, physical tests are carried out on the soil samples to obtain various parameter indicators, wherein the physical tests at least include particle grading analysis test, permeability coefficient test, water supply test and water storage rate test;

[0042] Then, the soil sample property classification is determined according to the particle grading, and the distribution model of soil permeability coefficient, water storage rate and water supply degree in the target area is established in combination with the soil permeability coefficient, water storage rate and water supply degree.

[0043] During the cross-section sampling process, key monitoring sections in the target study area can be selected, and soil samples can be extracted from points in the section using a drilling rig. The drilling depth can be adjusted according to the soil conditions in the study area. For example, in the embodiment of this case, 3*3 cross-section points are used for soil sample extraction in the preset target area, and the drilling depth is 12 to 16 meters. The specific values ​​can be adjusted according to the actual sampling conditions.

[0044] In the physical tests on soil samples, a laser particle size analyzer can be used with the assistance of a rock and soil mineralization analyzer to conduct a particle grading analysis test to obtain the percentage of clay, silt and sand in the soil sample and the mineral composition of the soil sample; and a constant head permeability test or a variable head permeability test can be used to determine the permeability coefficient of the soil sample.

[0045] In order to determine the soil distribution of the strata, the MS2000 laser particle size analyzer and the EVO 18 geotechnical mineralization analyzer were used to carry out particle and mineralized material analysis tests to obtain the percentage of clay, silt, sand and other contents of the soil samples and the mineral composition of the soil samples. Further, the spatial distribution of the strata can be divided according to the vertical depth of the elevation according to the classification standard of soil in the geological survey specification for embankment engineering.

[0046] Permeability coefficient is an important mechanical index of soil. Different test methods should be used to measure the permeability coefficient of different soil types. The test types can be divided into constant head permeability test and variable head permeability test. The constant head permeability test is suitable for coarse-grained soil, and the variable head permeability test is suitable for fine-grained soil. In the embodiment of this case, the SLB-1 stress-strain controlled triaxial test permeameter can also be used to truly simulate the permeability characteristics of the soil under a certain confining pressure environment.

[0047] Furthermore, the spatial distribution characteristics of soil sample parameters in the target area can be extracted and designed to include the following contents:

[0048] Firstly, according to the distribution model of three parameters, namely permeability coefficient, water storage rate and water supply degree, the partition of each parameter is determined;

[0049] Then, the three parameter distribution models are integrated and a probability distribution model is established, and the spatial distribution characteristics of soil samples in the target area are extracted based on the probability distribution model.

[0050] The distribution models of the three parameters of permeability, water supply and water storage rate are determined to determine the zoning of each parameter; the distribution models of the three parameters are integrated to establish a probability distribution model; the distribution characteristics are extracted according to the probability distribution model, the distribution model is integrated, and the study area is reasonably divided into zones to reflect the spatial distribution characteristics of the soil parameters in the study area.

[0051] S2, draw the probability distribution curve of hydrogeological multi-parameters, extract and combine the probability distribution function of related parameters, the value range and standard deviation of the parameter median confidence interval, and obtain the multi-parameter combination characteristic value.

[0052] Among them, drawing the hydrogeological multi-parameter probability distribution curve also includes: selecting the continuous distribution density function of each parameter according to the spatial heterogeneity characteristics of each parameter, wherein the continuous distribution density function includes: uniform distribution, exponential distribution, normal distribution or t distribution.

[0053] According to the field and indoor test data, data analysis was carried out on parameters such as permeability coefficient, water supply degree and water storage coefficient in the study area. By establishing a probability distribution function, the characteristic values ​​of relevant parameters were obtained as the input values ​​of hydrogeological parameters, and the accuracy of the groundwater numerical model based on multi-parameter probability distribution was analyzed.

[0054] According to the characteristics of random variables, random variables are usually divided into discrete and continuous types. Continuous random variables are mainly used to describe continuously changing geological phenomena, such as physical parameters such as porosity, permeability, and saturation. Soil permeability, water supply, and water storage rate all have the characteristics of continuous random variables. The corresponding continuous distribution density function can be selected to generate the parameter field. Common continuous distribution density functions include uniform distribution, exponential distribution, normal distribution, t distribution, etc.

[0055] Normal distribution, also known as Gaussian distribution, is a very important probability distribution in mathematics, physics, engineering and other fields, and has a significant influence on many aspects of statistics. The normal distribution with mathematical expectation and variance is denoted as X~N(μ,σ 2 ), its probability density function and cumulative distribution function are:

[0056]

[0057]

[0058] The t distribution is used to estimate the mean of a population with a normal distribution and unknown variance based on a small sample. If the population variance is known (for example, when the sample size is large enough), the normal distribution should be used to estimate the population mean. The shape of the t distribution curve is related to the size of the degrees of freedom n. Compared with the standard normal distribution curve, the smaller the degrees of freedom n, the flatter the t distribution curve, the lower the middle of the curve, and the higher the tails on both sides of the curve; the larger the degrees of freedom n, the closer the t distribution curve is to the normal distribution curve. When the degrees of freedom n is, the t distribution curve is the standard normal distribution curve. Assuming that X obeys the standard normal distribution and Y obeys the chi-square distribution, the distribution of is called the t distribution with n degrees of freedom, denoted by. Here, if k independent random variables all obey the standard normal distribution, then the sum of the squares of these k random variables that obey the standard normal distribution constitutes a new random variable X, whose distribution law is called the chi-square distribution, denoted by, where n represents the degrees of freedom. The distribution density function of the t distribution is:

[0059]

[0060] Here, Gam(x) is the gamma function.

[0061] The probability density function and cumulative distribution function of the exponential distribution are:

[0062]

[0063]

[0064] The single-sample KS test can be used to test whether the sample comes from a specific theoretical distribution. It compares the cumulative frequency distribution of the sample data with the specific theoretical distribution. If the difference between the two is very small, it is inferred that the sample is taken from a specific distribution. The judgment standard of the single-sample KS test is significance. If the significance is greater than 0.05, it can be considered that the sample is taken from the specific distribution.

[0065] In this case, according to the hydrogeological conditions of the study area, combined with the hydrogeological data of the study area and the results of indoor and outdoor tests, it is found that the permeability coefficient, water supply degree and water storage rate of the soil layer in the study area all have spatial heterogeneity characteristics. Therefore, through statistical analysis of the test results, the probability distribution function of the relevant parameters can be obtained.

[0066] S3, taking the multi-parameter combination eigenvalues ​​as the input of the finite difference analysis software, and using the finite difference analysis software to calculate the groundwater seepage, so as to obtain the groundwater flow field model under the multi-parameter probability distribution.

[0067] Among them, Visual MODFLOW Flex is more than just a graphical user interface for MODFLOW groundwater simulation. Visual MODFLOW Flex is also an industry standard specification software that integrates groundwater flow and contaminant transport, basic analysis and calibration tools, and powerful 3D visualization capabilities in a single, easy-to-use software environment. Using VisualMODFLOW Flex, the tools embedded in the software can be used to solve water quality, groundwater recharge, and water source protection problems within a local area. Therefore, in this embodiment, Visual MODFLOW Flex can be used as a finite difference analysis software to build a model.

[0068] Specifically, the calculation of groundwater seepage using finite difference analysis software can be designed to include the following contents:

[0069] Firstly, the target area is meshed using finite difference analysis software, and the interval to be solved is divided into a finite number of grid nodes. The groundwater numerical model of the target area is constructed using the set of finite grid nodes, and the difference quotient of the node function value is used to establish the difference equation group.

[0070] Then, the initial and boundary conditions are used to solve the differential equations to obtain the groundwater flow field model in the target area.

[0071] Among them, the differential equations can be solved using initial conditions and boundary conditions based on the parameter partitioning and value selection provided by the parameter space random distribution theoretical model and combined with the simulated hydrogeological conditions and source-sink characteristics of the target area.

[0072] S4, the model parameters are calibrated using the parameter median as the initial parameter of the model and the confidence interval value range as the parameter inversion range to finally determine the groundwater flow field model parameters.

[0073] The parameter median is used as the initial model parameter, and the confidence interval value range is used as the parameter inversion range. The confidence interval can be set with a confidence level of 95%. The model parameters are calibrated using the PEST inversion module embedded in the Visual MODFLOW Flex software to finally determine the model parameters.

[0074] Furthermore, based on the above method, an embodiment of the present invention also provides a groundwater flow field model optimization system based on multi-parameter probability distribution, comprising: a feature extraction module, a feature combination module, a model construction module and a parameter determination module, wherein:

[0075] A feature extraction module is used to establish a hydrogeological multi-parameter distribution model of soil samples in the target area to extract the spatial distribution characteristics of soil sample parameters in the target area, wherein the hydrogeological multi-parameters include permeability, water supply and water storage rate;

[0076] The feature combination module is used to draw the probability distribution curve of hydrogeological multi-parameters, extract and combine the probability distribution function of related parameters, the value range and standard deviation of the parameter median confidence interval, and obtain the multi-parameter combination feature value;

[0077] A model building module is used to input the multi-parameter combination eigenvalues ​​into the finite difference analysis software, and calculate the groundwater seepage using the finite difference analysis software to obtain a groundwater flow field model under the multi-parameter probability distribution;

[0078] The parameter determination module is used to calibrate the model parameters by using the parameter median as the initial parameter of the model and the confidence interval value range as the parameter inversion range, so as to finally determine the groundwater flow field model parameters.

[0079] In order to verify the effectiveness of this solution, the following is a further explanation based on the test data:

[0080] The preset target simulation area is set to be the 16th section of the Qingshui River where key monitoring is performed. The numerical model is shown in the figure below: Figure 2 As shown, the groundwater type in the site is Quaternary loose layer pore groundwater, which is mainly found in sandy loam, light silt loam and silt-fine sand layers. The groundwater in the lower silt-fine sand layer is pressure-bearing; sandy loam and silt-fine sand have medium permeability, light silt loam has weak to medium permeability, and heavy silt loam has weak permeability and is a relatively impermeable layer. During the exploration period, the groundwater depth was generally 3 to 5 meters. The groundwater has dynamic characteristics, and the amplitude is generally 1 to 3 meters. The groundwater in the site is mainly recharged by atmospheric precipitation, lateral runoff and infiltration of local river sections, and consumed by evaporation, mining, lateral runoff and river discharge. The river water and groundwater in the site replenish each other. When the river water level is high, the river water replenishes the groundwater, and when the river water level is low, the groundwater replenishes the river water.

[0081] Taking the first layer of heavy silt loam as an example, the vertical permeability coefficient conforms to the exponential distribution, the horizontal permeability coefficient conforms to the t distribution, the water supply degree also conforms to the t distribution, and the water storage rate conforms to the exponential distribution. Since the permeability coefficient, water supply degree, and water storage rate values ​​all span different orders of magnitude, we take their logarithms and then perform statistical analysis, such as Figures 3 to 5 shown.

[0082] Table 1 shows the characteristic parameter values ​​of the probability distribution function of the permeability coefficient of different soils. Through the KS test, it is found that the distribution function has a high degree of fit and the relevant parameters of each soil layer have obvious distribution characteristics.

[0083] Table 1 Parameter distribution fitting table

[0084]

[0085]

[0086] The key parameters of this numerical simulation include the permeability coefficient, water supply degree and water storage coefficient of the aquifer. According to the test results, the probability distribution statistics of the parameters are carried out by soil layer, and the probability distribution curves of the permeability coefficient, water supply degree and water storage coefficient of different soil layers are obtained. The parameter median and confidence interval (confidence level 95%) are calculated, as shown in Table 2.

[0087]

[0088] The generalization scope of this model is not an independent and complete hydrogeological unit. To facilitate numerical simulation, the Qingshui River on the west side of the horizontal generalization boundary is used as the fixed head boundary, and the serialized monitoring wells on the east side are generalized as the given head boundary. The parts perpendicular to the fixed head boundary (north and south sides) are generalized as water-isolating boundaries (i.e., zero flow boundaries).

[0089] Vertically, the aquifers in this study area are the I, II, III, IV, and V aquifers. The free water surface of the shallow groundwater aquifer is the top plate boundary of the groundwater numerical simulation. Through this boundary, the shallow groundwater exchanges vertical water with the outside of the system. The clay layer and silty clay layer between the shallow water layer and the deep confined water layer serve as the water-proof bottom plate of this model.

[0090] The dynamics of groundwater in the model are mainly affected by the input of sink and source items. The source and sink items in the simulation area include three elements: point, line, and surface. Point elements are source and sink items such as agricultural exploitation; line elements mainly represent river recharge items (processed by the River module); surface elements are given by the Recharge module, mainly representing recharge items such as rainfall infiltration and irrigation infiltration. Other source and sink items such as groundwater evaporation are brought into the model calculation through the Well module and are all processed into exploitation or recharge wells.

[0091] Due to insufficient hydrogeological data in the study area, the initial flow field cannot be directly obtained. Based on the existing elevation data, rainfall data, basin inflow and outflow data, and hydrogeological parameter data of each aquifer in the study area, the steady flow in 2022 was simulated to obtain the initial flow field of the steady flow.

[0092] According to the hydrogeological conceptual model, the hydrogeological parameters and other boundary conditions are input into the model. Based on the parameter partitioning and value selection method provided by the theoretical model of spatial random distribution of soil parameters, the initial conditions and boundary conditions are used to solve the equations for the hydrogeological conditions and source-sink characteristics of the simulation area to obtain the numerical model of groundwater flow field in the study area, such as Figure 6 shown.

[0093] In order to better reflect the advantages of considering multi-parameter probability distribution models, groundwater model I and groundwater model II were established respectively.

[0094] In model I, the hydrogeological parameters were based on the recommended values ​​of geological survey; in model II, the median of the parameters was used as the initial model parameters, and the confidence interval (95% confidence level) was used as the parameter inversion range. The model parameters were calibrated using the PEST inversion module embedded in the Visual MODFLOW Flex software, and the model parameters were finally determined. The boundary, source and sink terms, time step and other conditions were kept unchanged.

[0095] The simulation results of Model I and Model II were matched and analyzed with the actual groundwater monitoring results under the corresponding working conditions. The corresponding matching related fitting degrees are as follows: Figure 7 shown.

[0096] By comparing the simulated water level with the observed water level, it can be seen that the maximum difference between the calculated water level of model I and the observed water level occurred at QSH5-3 ​​on October 24, 2022, with a difference of 127cm, and the minimum difference occurred at QSH5-3 ​​on January 24, 2023, with a difference of 69cm; the maximum difference between the calculated water level of model II and the observed water level occurred at QSH5-3 ​​on October 24, 2022, with a difference of 27cm, a decrease of 78.7%, and the minimum difference occurred at QSH5-2 on December 24, 2022, with a difference of 0.9cm, a decrease of 98.7%.

[0097] The standard deviations of the water level calculated by model I and the observation wells were 1.11, 0.92, and 0.77, respectively, and the standard deviations of the corresponding points in model II and the actual observation values ​​of the observation wells were 0.96, 0.72, and 0.52, respectively. By considering the random distribution of multiple parameters, the standard deviation of the water level calculated by the model and the actual observation values ​​of the observation wells can be comprehensively reduced by 0.20.

[0098] Therefore, the above data can verify that the scheme in this case takes into account multiple parameters, making the calculation results of the randomly distributed numerical model closer to the actual observation values, and the model fit is higher. It can optimize and improve the expected effect of the model simulation and has good application prospects in the field of environmental geological research.

[0099] Unless otherwise specifically stated, the relative steps, numerical expressions and values ​​of the components and steps set forth in these embodiments do not limit the scope of the present invention.

[0100] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0101] The units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person of ordinary skill in the art may use different methods to implement the described functions for each specific application, but such implementation is not considered to be beyond the scope of the present invention.

[0102] Those skilled in the art will appreciate that all or part of the steps in the above method can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a disk or an optical disk. Optionally, all or part of the steps in the above embodiment can also be implemented using one or more integrated circuits, and accordingly, each module / unit in the above embodiment can be implemented in the form of hardware or in the form of software function modules. The present invention is not limited to any specific form of combination of hardware and software.

[0103] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A groundwater flow field model optimization method based on multi-parameter probability distribution, characterized in that: Include: Establish a hydrogeological multi-parameter distribution model of soil samples in the target area to extract the spatial distribution characteristics of soil sample parameters in the target area, where the hydrogeological multi-parameters include permeability, water supply and water storage rate; Establish a hydrogeological multi-parameter distribution model, including: first, Conduct on-site exploration, drilling and stratified sampling of the target area section to obtain soil samples; then, conduct physical tests on the soil samples to obtain various parameter indicators, wherein the physical tests at least include particle grading analysis test, permeability test, water supply test and water storage test; in the physical test of the soil samples, use a laser particle size analyzer and assist with a rock and soil mineralization analyzer to conduct a particle grading analysis test to obtain the percentage of clay, silt and sand content of the soil samples and the mineral composition of the soil samples; and use a constant head permeability test or a variable head permeability test to determine the permeability of the soil samples. Then, determine the classification of soil sample properties based on the particle grading, and establish a distribution model of the permeability coefficient, water storage rate and water supply rate of the target area soil combined with the permeability coefficient, water storage rate and water supply rate of the soil sample; and extract the spatial distribution characteristics of the parameters of the soil samples in the target area, including: first, determine the partition of each parameter based on the distribution model of the three parameters of permeability coefficient, water storage rate and water supply rate; then, integrate the three parameter distribution models and establish a probability distribution model, and extract the spatial distribution characteristics of the soil samples in the target area based on the probability distribution model; Draw the probability distribution curve of hydrogeological multi-parameters, extract and combine the probability distribution function of related parameters, the value range and standard deviation of the parameter median confidence interval, and obtain the multi-parameter combination characteristic value; The multi-parameter combination eigenvalues ​​are used as inputs to the finite difference analysis software, and the groundwater seepage is calculated using the finite difference analysis software to obtain a groundwater flow field model under the multi-parameter probability distribution; wherein, the groundwater seepage is calculated using the finite difference analysis software, including: first, the target area is meshed using the finite difference analysis software, the interval to be calculated is divided into a finite number of grid nodes, the groundwater numerical model of the target area is constructed using the set of the finite number of grid nodes, and a differential equation group is established using the difference quotient of the node function value; then, the differential equation group is solved using the initial conditions and boundary conditions, based on the parameter partitioning and value selection provided by the parameter space random distribution theoretical model, and combined with the simulated hydrogeological conditions and source-sink characteristics of the target area, the differential equation group is solved using the initial conditions and boundary conditions to obtain the groundwater flow field model of the target area; The model parameters were calibrated using the median of the parameters as the initial parameters of the model and the confidence interval value range as the parameter inversion range to ultimately determine the groundwater flow field model parameters.

2. The groundwater flow field model optimization method based on multi-parameter probability distribution according to claim 1 is characterized in that: Drawing the probability distribution curve of hydrogeological multiple parameters also includes: selecting the continuous distribution density function of each parameter according to the spatial heterogeneity characteristics of each parameter, wherein the continuous distribution density function includes: uniform distribution, exponential distribution, normal distribution or t distribution.

3. A groundwater flow field model optimization system based on multi-parameter probability distribution, characterized in that: The method according to claim 1 is implemented, comprising: a feature extraction module, a feature combination module, a model construction module and a parameter determination module, wherein: A feature extraction module is used to establish a hydrogeological multi-parameter distribution model of soil samples in the target area to extract the spatial distribution characteristics of soil sample parameters in the target area, wherein the hydrogeological multi-parameters include permeability, water supply and water storage rate; The feature combination module is used to draw the probability distribution curve of hydrogeological multi-parameters, extract and combine the probability distribution function of related parameters, the value range and standard deviation of the parameter median confidence interval, and obtain the multi-parameter combination feature value; A model building module is used to input the multi-parameter combination eigenvalues ​​into the finite difference analysis software, and calculate the groundwater seepage using the finite difference analysis software to obtain a groundwater flow field model under the multi-parameter probability distribution; The parameter determination module is used to calibrate the model parameters by using the parameter median as the initial parameter of the model and the confidence interval value range as the parameter inversion range, so as to finally determine the groundwater flow field model parameters.

4. An electronic device, characterized in that: It includes a memory and a processor, and the processor and the memory communicate with each other through a bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the method according to any one of claims 1 to 2.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 2 is implemented.

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